Loading Everything
What features to take?
- Not sure to take Zone Entropy or not
- Run Entropy and Zone Entropy do not follow the same patter on biplot


Important measures to check
- We will compare diameter to volume to ITH
pyrad$volume = as.numeric(pyrad$lesion1sizepath)
pyrad$volume_from_pyrad = as.numeric(pyrad$original_shape_MeshVolume)
pyrad$diameter = as.numeric(pyrad$original_shape_Maximum2DDiameterSlice)
how to define radITH
- Do we need to normalize something by volume?
- Numbers were a bit wierd when divided by volume therefore I did not divide anything with volume
pyrad$radITH = rowMeans(pyrad[,features_of_interest], na.rm = T)
Q = 3
pyrad$volume_group = gtools::quantcut(pyrad$volume, q=Q, na.rm=TRUE)
pyrad$diameter_group = gtools::quantcut(pyrad$diameter, q=Q, na.rm=TRUE)
pyrad$radITH_group = gtools::quantcut(pyrad$radITH, q=Q, na.rm=TRUE)
Expected correlations
- Negative cor radITH to volume
- Positive cor to ORACLE(not true)
- Volume and diameter are correlated to volume therefore we are probably not confunded by volume
- We would expect poz. cor. to Heterogenious but not mandatory since we do not know if radITH is the same as bioITH


Mutations
Let’s group DRIVER mutations by Sanchez Vega def
Let’s test Sanchez Vega Muts vs radITH groups (q =3)
- It seems Adeno is associated with wtn, cell_cycle,pi3k
- It seems Squamous is associated with pi3k and RTK/KRAS
## [1] "Adeno fisher test results"
## [1] "nrf2"
##
## Fisher's Exact Test for Count Data
##
## data: table(tmp$radITH_group, tmp[, col])
## p-value = 0.02679
## alternative hypothesis: two.sided
##
## [1] "pi3k"
##
## Fisher's Exact Test for Count Data
##
## data: table(tmp$radITH_group, tmp[, col])
## p-value = 0.003382
## alternative hypothesis: two.sided
##
## [1] "cell_cycle"
##
## Fisher's Exact Test for Count Data
##
## data: table(tmp$radITH_group, tmp[, col])
## p-value = 0.0003902
## alternative hypothesis: two.sided
##
## [1] "wnt"
##
## Fisher's Exact Test for Count Data
##
## data: table(tmp$radITH_group, tmp[, col])
## p-value = 0.04017
## alternative hypothesis: two.sided
## [1] "Squamous fisher test results"
## [1] "rtk_kras"
##
## Fisher's Exact Test for Count Data
##
## data: table(tmp$radITH_group, tmp[, col])
## p-value = 0.01748
## alternative hypothesis: two.sided
##
## [1] "pi3k"
##
## Fisher's Exact Test for Count Data
##
## data: table(tmp$radITH_group, tmp[, col])
## p-value = 0.007167
## alternative hypothesis: two.sided
Does Volume or diameter predict biology?
- Diameter is not associated at all
- Volume is associated with HIPPO
## [1] "Adeno fisher test results"
## [1] "Squamous fisher test results"
## [1] "hippo"
##
## Fisher's Exact Test for Count Data
##
## data: table(tmp$volume_group, tmp[, col])
## p-value = 0.01164
## alternative hypothesis: two.sided
Can we overlap radITH and Volume groups and check survival?
Coxph Model
- radITH does not help improve cox ph model

Hallmarks all samples
- Association (cor) of radITH with hallmarks
- Hallmarks computed with SS-GSEA and GSVA
- P values are adjusted using FDR

Hallmarks Adeno
- No significant correlations

Hallmarks Squamous
- No significant correlations

Hallmark expression-radITH Correlation in Large vs Small tumors (all samples)
- Large tumors seem to harbour association between radITH and biology
- Small tumors not significant hits


Let’s split by Size and Pathology and repeat
- Large Squamous cell tumors show significant hits in DNA repair and MYC targets v2
- Small Squmous, no significant hits
- Small/Big Adeno tumors do not show any significant hits

Picking genes for Gene Expression Analysis
- Mean and SD value based on entire cohort

## Number of Genes after cutoff: 10332
Gene Expression Analysis (Squamous)
- Genes that were picked for analysis were based on mean and SD (entire cohort)



Gene Expression Analysis (Adeno)
- Genes that were picked for analysis were based on mean and SD (entire cohort)


